Introduction

This notebook will try to predict BTC price through:

• RNN: Recurrent Neural Network
• LSTM: Long Short Term Memory
• ES: Early Stopping
• Single-Step and Multi-Step time series forecasting
• Univariate (only input Price) and Multivariate (inputs Price and Volume)

Parameter settings

# number of total past observations from the original dataset to be considered
n_past_total = 1200 #1500 #2000

# number of past observations to be considered for the LSTM training and prediction
n_past = 120 #30 #50

# number of future datapoints to predict (if higher than 1, the model switch to Multi-Step)
n_future = 30 #10

# activation function used for the RNN (softsign, relu, sigmoid)
activation = 'softsign'

# dropout for the hidden layers
dropout = 0.2

# number of hidden layers
n_layers = 6

# number of neurons of the hidden layers
n_neurons = 15

# features to be considered for training (if only one is Close, then its Univariate, if more, then it's Multivariate)
features = ['Close', 'Volume']

# number of inputs features (if higher than 1, )
n_features = len(features)

# patience for the early stopping (number of epochs)
patience = 25

# optimizer (adam, RMSprop)
optimizer='adam'

Importing libraries

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from keras.models import Sequential, load_model
from keras.layers import Dense, LSTM, Dropout
from sklearn.preprocessing import MinMaxScaler
pd.set_option('display.float_format', lambda x: '%.3f' % x) #avoid scientific notation
np.set_printoptions(suppress=True)
import datetime
import math
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score
from keras.callbacks import EarlyStopping
#import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from IPython.display import Image

Importing the files

# reading csv
dataset = pd.read_csv('yahoo_BTC-USD.csv')
dataset

	Date	Open	High	Low	Close	Adj Close	Volume
0	2014-09-17	465.864	468.174	452.422	457.334	457.334	21056800.000
1	2014-09-18	456.860	456.860	413.104	424.440	424.440	34483200.000
2	2014-09-19	424.103	427.835	384.532	394.796	394.796	37919700.000
3	2014-09-20	394.673	423.296	389.883	408.904	408.904	36863600.000
4	2014-09-21	408.085	412.426	393.181	398.821	398.821	26580100.000
...	...	...	...	...	...	...	...
2449	2021-06-01	37293.793	37896.734	35787.086	36684.926	36684.926	34639423297.000
2450	2021-06-02	36699.922	38231.340	35966.309	37575.180	37575.180	33070867190.000
2451	2021-06-03	37599.410	39478.953	37243.973	39208.766	39208.766	35460750427.000
2452	2021-06-04	39242.484	39242.484	35717.723	36894.406	36894.406	41831090187.000
2453	2021-06-05	36780.348	37908.945	35548.820	35746.102	35746.102	34500784128.000

2454 rows × 7 columns

EDA (brief)

# checking for nulls
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2454 entries, 0 to 2453
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       2454 non-null   object 
 1   Open       2450 non-null   float64
 2   High       2450 non-null   float64
 3   Low        2450 non-null   float64
 4   Close      2450 non-null   float64
 5   Adj Close  2450 non-null   float64
 6   Volume     2450 non-null   float64
dtypes: float64(6), object(1)
memory usage: 134.3+ KB

# checking if close is not equal to adj close
dataset[dataset['Close']!=dataset['Adj Close']]

	Date	Open	High	Low	Close	Adj Close	Volume
2039	2020-04-17	nan	nan	nan	nan	nan	nan
2214	2020-10-09	nan	nan	nan	nan	nan	nan
2217	2020-10-12	nan	nan	nan	nan	nan	nan
2218	2020-10-13	nan	nan	nan	nan	nan	nan

# checking the main parameters
dataset.describe()

	Open	High	Low	Close	Adj Close	Volume
count	2450.000	2450.000	2450.000	2450.000	2450.000	2450.000
mean	7626.843	7844.916	7387.271	7640.137	7640.137	12616231026.884
std	11665.490	12032.959	11232.481	11676.218	11676.218	19727883639.959
min	176.897	211.731	171.510	178.103	178.103	5914570.000
25%	460.801	466.735	455.070	460.719	460.719	68730922.000
50%	4324.400	4403.835	4138.012	4330.050	4330.050	4010640000.000
75%	9193.489	9355.582	9030.895	9203.736	9203.736	18684308124.500
max	63523.754	64863.098	62208.965	63503.457	63503.457	350967941479.000

# use close only and fill NaN with ffil
df = dataset.set_index('Date')[features]#.tail(n_past_total)
df = df.set_index(pd.to_datetime(df.index))
df.fillna(method='ffill',inplace=True)

# looking at the correlation of the main possible variables
dataset[['Close','Volume']].corr()

	Close	Volume
Close	1.000	0.798
Volume	0.798	1.000

# plotting Closing Price and Volume
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(go.Scatter(x=df[features[0]].index, y=df[features[0]].values, name=features[0]), secondary_y=False)

fig.add_trace(go.Scatter(x=df[features[1]].index, y=df[features[1]].values, name=features[1]), secondary_y=True)

# Add figure title
fig.update_layout(title_text="BTC: {}, {}".format(features[0], features[1]))

# Set x-axis title
fig.update_xaxes(title_text='<b>'+df.index.name+'</b>')

# Set y-axes titles
fig.update_yaxes(title_text='<b>'+features[0]+'</b>', secondary_y=False)
fig.update_yaxes(title_text='<b>'+features[1]+'</b>', secondary_y=True)

# Adding slider
fig.update_xaxes(
    rangeslider_visible=True,
    rangeselector=dict(
        buttons=list([
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(count=1, label="YTD", step="year", stepmode="todate"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)

fig.show()

We can observe a spike in volume around end of Feb2021. Let's take a look.

# looking at Feb21 datapoints
df[(df.index>'2021-02-01') & (df.index<'2021-03-01')].style.bar(subset=['Volume'], color='#d65f5f')

	Close	Volume
Date
2021-02-02 00:00:00	35510.289063	63088585433.000000
2021-02-03 00:00:00	37472.089844	61166818159.000000
2021-02-04 00:00:00	36926.066406	68838074392.000000
2021-02-05 00:00:00	38144.308594	58598066402.000000
2021-02-06 00:00:00	39266.011719	71326033653.000000
2021-02-07 00:00:00	38903.441406	65500641143.000000
2021-02-08 00:00:00	46196.464844	101467222687.000000
2021-02-09 00:00:00	46481.105469	91809846886.000000
2021-02-10 00:00:00	44918.183594	87301089896.000000
2021-02-11 00:00:00	47909.332031	81388911810.000000
2021-02-12 00:00:00	47504.851563	76555041196.000000
2021-02-13 00:00:00	47105.515625	70250456155.000000
2021-02-14 00:00:00	48717.289063	71248675228.000000
2021-02-15 00:00:00	47945.058594	77069903166.000000
2021-02-16 00:00:00	49199.871094	77049582886.000000
2021-02-17 00:00:00	52149.007813	80820545404.000000
2021-02-18 00:00:00	51679.796875	52054723579.000000
2021-02-19 00:00:00	55888.132813	63495496918.000000
2021-02-20 00:00:00	56099.519531	68145460026.000000
2021-02-21 00:00:00	57539.945313	51897585191.000000
2021-02-22 00:00:00	54207.320313	92052420332.000000
2021-02-23 00:00:00	48824.425781	106102492824.000000
2021-02-24 00:00:00	49705.332031	63695521388.000000
2021-02-25 00:00:00	47093.851563	54506565949.000000
2021-02-26 00:00:00	46339.761719	350967941479.000000
2021-02-27 00:00:00	46188.453125	45910946382.000000
2021-02-28 00:00:00	45137.769531	53443887451.000000

After checking from a different source, the Volume for 2021-02-26 seems to be correct (USD 350.967.941.479). Looks like that day was crazy. https://coinmarketcap.com/historical/20210226/

Image('img/coinmarketcap-20210226.png')

Preprocessing the data

# removing firsts observations becuase they may not be representative of BTC behaviour now due to beginnings of crypto market
df = df.tail(n_past_total)

# train test split
#n_future = 10
df_train = df.head(-n_future)
df_test = df.tail(n_future)
training_set = df_train.values
test_set = df_test.values
print('training_set.shape:\t', training_set.shape)
print('test_set.shape:\t\t', test_set.shape)

training_set.shape:	 (1170, 2)
test_set.shape:		 (30, 2)

# scale
sc = MinMaxScaler(feature_range=(0,1))
training_set_scaled = sc.fit_transform(training_set)
print('training_set_scaled.shape: ', training_set_scaled.shape)
training_set_scaled

training_set_scaled.shape:  (1170, 2)

array([[0.11230479, 0.01470046],
       [0.11721794, 0.01383683],
       [0.10912011, 0.0114763 ],
       ...,
       [0.83125144, 0.18859968],
       [0.89912423, 0.19054371],
       [0.88207514, 0.19135386]])

# creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []

for i in range(n_past, len(training_set_scaled) - n_future + 1):
    X_train.append(training_set_scaled[i-n_past:i, :])
    y_train.append(training_set_scaled[i:i+n_future, 0])

X_train, y_train = np.array(X_train), np.array(y_train)
X_train.shape, y_train.shape

((1021, 120, 2), (1021, 30))

# reshaping (needed to fit RNN)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], n_features))
X_train.shape

(1021, 120, 2)

RNN

Build and fit

# Building the RNN

# Initialising the RNN
regressor = Sequential()

# Input layer
regressor.add(LSTM(units=n_past, return_sequences=True, activation=activation, input_shape=(X_train.shape[1], n_features))) 
#regressor.add(LSTM(units=neurons, return_sequences=True, activation=activation, input_shape=(X_train.shape[1], 1))) 

# Hidden layers
for _ in range(n_layers):
    regressor.add(Dropout(dropout))
    regressor.add(LSTM(units=n_neurons, return_sequences=True, activation=activation))

# Last hidden layer (changing the return_sequences)
regressor.add(LSTM(units=n_neurons, return_sequences=False, activation=activation))
regressor.add(Dropout(dropout))

# Adding the output layer
regressor.add(Dense(units=n_future))

# Compiling the RNN
regressor.compile(optimizer=optimizer, loss='mse', metrics=['accuracy'])

# Model summary
regressor.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 120, 120)          59040     
_________________________________________________________________
dropout (Dropout)            (None, 120, 120)          0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 120, 15)           8160      
_________________________________________________________________
dropout_1 (Dropout)          (None, 120, 15)           0         
_________________________________________________________________
lstm_2 (LSTM)                (None, 120, 15)           1860      
_________________________________________________________________
dropout_2 (Dropout)          (None, 120, 15)           0         
_________________________________________________________________
lstm_3 (LSTM)                (None, 120, 15)           1860      
_________________________________________________________________
dropout_3 (Dropout)          (None, 120, 15)           0         
_________________________________________________________________
lstm_4 (LSTM)                (None, 120, 15)           1860      
_________________________________________________________________
dropout_4 (Dropout)          (None, 120, 15)           0         
_________________________________________________________________
lstm_5 (LSTM)                (None, 120, 15)           1860      
_________________________________________________________________
dropout_5 (Dropout)          (None, 120, 15)           0         
_________________________________________________________________
lstm_6 (LSTM)                (None, 120, 15)           1860      
_________________________________________________________________
lstm_7 (LSTM)                (None, 15)                1860      
_________________________________________________________________
dropout_6 (Dropout)          (None, 15)                0         
_________________________________________________________________
dense (Dense)                (None, 30)                480       
=================================================================
Total params: 78,840
Trainable params: 78,840
Non-trainable params: 0
_________________________________________________________________

# Adding early stopping
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=patience)

# Fitting the RNN to the Training set
res = regressor.fit(X_train, y_train
                    , batch_size=32
                    , epochs=750
                    , validation_split=0.1
                    , callbacks=[early_stop]
                   )

Epoch 1/750
29/29 [==============================] - 32s 294ms/step - loss: 0.0116 - accuracy: 0.0336 - val_loss: 0.5116 - val_accuracy: 0.0874
Epoch 2/750
29/29 [==============================] - 6s 224ms/step - loss: 0.0079 - accuracy: 0.0470 - val_loss: 0.4471 - val_accuracy: 0.1165
Epoch 3/750
29/29 [==============================] - 6s 219ms/step - loss: 0.0051 - accuracy: 0.0381 - val_loss: 0.3788 - val_accuracy: 0.1165
Epoch 4/750
29/29 [==============================] - 6s 217ms/step - loss: 0.0044 - accuracy: 0.0262 - val_loss: 0.2809 - val_accuracy: 0.1165
Epoch 5/750
29/29 [==============================] - 6s 218ms/step - loss: 0.0029 - accuracy: 0.0349 - val_loss: 0.1533 - val_accuracy: 0.1165
Epoch 6/750
29/29 [==============================] - 6s 217ms/step - loss: 0.0022 - accuracy: 0.0302 - val_loss: 0.0953 - val_accuracy: 0.1165
Epoch 7/750
29/29 [==============================] - 6s 220ms/step - loss: 0.0018 - accuracy: 0.0414 - val_loss: 0.0913 - val_accuracy: 0.1165
Epoch 8/750
29/29 [==============================] - 6s 224ms/step - loss: 0.0020 - accuracy: 0.0300 - val_loss: 0.0981 - val_accuracy: 0.1165
Epoch 9/750
29/29 [==============================] - 7s 231ms/step - loss: 0.0016 - accuracy: 0.0357 - val_loss: 0.0886 - val_accuracy: 0.1165
Epoch 10/750
29/29 [==============================] - 7s 230ms/step - loss: 0.0014 - accuracy: 0.0381 - val_loss: 0.0909 - val_accuracy: 0.1165
Epoch 11/750
29/29 [==============================] - 7s 232ms/step - loss: 0.0011 - accuracy: 0.0557 - val_loss: 0.0812 - val_accuracy: 0.1165
Epoch 12/750
29/29 [==============================] - 7s 231ms/step - loss: 0.0012 - accuracy: 0.0407 - val_loss: 0.0907 - val_accuracy: 0.1165
Epoch 13/750
29/29 [==============================] - 7s 232ms/step - loss: 0.0013 - accuracy: 0.0426 - val_loss: 0.0828 - val_accuracy: 0.1165
Epoch 14/750
29/29 [==============================] - 7s 253ms/step - loss: 0.0012 - accuracy: 0.0379 - val_loss: 0.0842 - val_accuracy: 0.0874
Epoch 15/750
29/29 [==============================] - 7s 229ms/step - loss: 0.0014 - accuracy: 0.0277 - val_loss: 0.0624 - val_accuracy: 0.0874
Epoch 16/750
29/29 [==============================] - 7s 241ms/step - loss: 0.0011 - accuracy: 0.0321 - val_loss: 0.0664 - val_accuracy: 0.1165
Epoch 17/750
29/29 [==============================] - 7s 236ms/step - loss: 9.8281e-04 - accuracy: 0.0461 - val_loss: 0.0472 - val_accuracy: 0.1165
Epoch 18/750
29/29 [==============================] - 7s 232ms/step - loss: 0.0010 - accuracy: 0.0423 - val_loss: 0.0786 - val_accuracy: 0.1165
Epoch 19/750
29/29 [==============================] - 7s 235ms/step - loss: 9.7445e-04 - accuracy: 0.0391 - val_loss: 0.0552 - val_accuracy: 0.0874
Epoch 20/750
29/29 [==============================] - 7s 235ms/step - loss: 0.0011 - accuracy: 0.0470 - val_loss: 0.0638 - val_accuracy: 0.0971
Epoch 21/750
29/29 [==============================] - 7s 230ms/step - loss: 9.2434e-04 - accuracy: 0.0289 - val_loss: 0.0454 - val_accuracy: 0.0291
Epoch 22/750
29/29 [==============================] - 7s 231ms/step - loss: 9.3791e-04 - accuracy: 0.0449 - val_loss: 0.0604 - val_accuracy: 0.0291
Epoch 23/750
29/29 [==============================] - 7s 229ms/step - loss: 0.0011 - accuracy: 0.0578 - val_loss: 0.0738 - val_accuracy: 0.1165
Epoch 24/750
29/29 [==============================] - 7s 229ms/step - loss: 8.8537e-04 - accuracy: 0.0458 - val_loss: 0.0662 - val_accuracy: 0.0291
Epoch 25/750
29/29 [==============================] - 7s 230ms/step - loss: 9.6977e-04 - accuracy: 0.0654 - val_loss: 0.0797 - val_accuracy: 0.0874
Epoch 26/750
29/29 [==============================] - 7s 228ms/step - loss: 8.2211e-04 - accuracy: 0.0296 - val_loss: 0.0582 - val_accuracy: 0.1165
Epoch 27/750
29/29 [==============================] - 7s 229ms/step - loss: 8.3923e-04 - accuracy: 0.0528 - val_loss: 0.0628 - val_accuracy: 0.0874
Epoch 28/750
29/29 [==============================] - 7s 229ms/step - loss: 8.4639e-04 - accuracy: 0.0449 - val_loss: 0.0671 - val_accuracy: 0.0874
Epoch 29/750
29/29 [==============================] - 7s 230ms/step - loss: 6.6406e-04 - accuracy: 0.0338 - val_loss: 0.0851 - val_accuracy: 0.0874
Epoch 30/750
29/29 [==============================] - 7s 230ms/step - loss: 7.3927e-04 - accuracy: 0.0543 - val_loss: 0.0672 - val_accuracy: 0.0874
Epoch 31/750
29/29 [==============================] - 7s 229ms/step - loss: 8.1294e-04 - accuracy: 0.0435 - val_loss: 0.0652 - val_accuracy: 0.0874
Epoch 32/750
29/29 [==============================] - 7s 227ms/step - loss: 7.0188e-04 - accuracy: 0.0283 - val_loss: 0.0713 - val_accuracy: 0.0874
Epoch 33/750
29/29 [==============================] - 7s 228ms/step - loss: 7.1076e-04 - accuracy: 0.0683 - val_loss: 0.0543 - val_accuracy: 0.0874
Epoch 34/750
29/29 [==============================] - 7s 238ms/step - loss: 7.6645e-04 - accuracy: 0.0407 - val_loss: 0.0415 - val_accuracy: 0.0874
Epoch 35/750
29/29 [==============================] - 7s 228ms/step - loss: 7.8250e-04 - accuracy: 0.0398 - val_loss: 0.0417 - val_accuracy: 0.0874
Epoch 36/750
29/29 [==============================] - 7s 228ms/step - loss: 7.4827e-04 - accuracy: 0.0278 - val_loss: 0.0698 - val_accuracy: 0.0874
Epoch 37/750
29/29 [==============================] - 7s 231ms/step - loss: 6.5613e-04 - accuracy: 0.0228 - val_loss: 0.0527 - val_accuracy: 0.0874
Epoch 38/750
29/29 [==============================] - 7s 228ms/step - loss: 5.8132e-04 - accuracy: 0.0449 - val_loss: 0.0640 - val_accuracy: 0.0874
Epoch 39/750
29/29 [==============================] - 7s 229ms/step - loss: 7.4772e-04 - accuracy: 0.0391 - val_loss: 0.0397 - val_accuracy: 0.0680
Epoch 40/750
29/29 [==============================] - 7s 232ms/step - loss: 7.7066e-04 - accuracy: 0.0566 - val_loss: 0.0658 - val_accuracy: 0.0874
Epoch 41/750
29/29 [==============================] - 7s 230ms/step - loss: 8.1629e-04 - accuracy: 0.0378 - val_loss: 0.0602 - val_accuracy: 0.0971
Epoch 42/750
29/29 [==============================] - 7s 227ms/step - loss: 6.9382e-04 - accuracy: 0.0646 - val_loss: 0.0373 - val_accuracy: 0.0777
Epoch 43/750
29/29 [==============================] - 7s 227ms/step - loss: 0.0012 - accuracy: 0.0779 - val_loss: 0.1193 - val_accuracy: 0.0874
Epoch 44/750
29/29 [==============================] - 7s 227ms/step - loss: 7.3846e-04 - accuracy: 0.0403 - val_loss: 0.0760 - val_accuracy: 0.0874
Epoch 45/750
29/29 [==============================] - 7s 227ms/step - loss: 7.4030e-04 - accuracy: 0.0327 - val_loss: 0.0615 - val_accuracy: 0.0874
Epoch 46/750
29/29 [==============================] - 7s 228ms/step - loss: 6.2122e-04 - accuracy: 0.0429 - val_loss: 0.0552 - val_accuracy: 0.0874
Epoch 47/750
29/29 [==============================] - 7s 227ms/step - loss: 5.5215e-04 - accuracy: 0.0382 - val_loss: 0.0477 - val_accuracy: 0.0874
Epoch 48/750
29/29 [==============================] - 7s 228ms/step - loss: 5.5723e-04 - accuracy: 0.0498 - val_loss: 0.0499 - val_accuracy: 0.0874
Epoch 49/750
29/29 [==============================] - 7s 228ms/step - loss: 6.6943e-04 - accuracy: 0.0617 - val_loss: 0.0450 - val_accuracy: 0.0874
Epoch 50/750
29/29 [==============================] - 7s 227ms/step - loss: 6.9006e-04 - accuracy: 0.0413 - val_loss: 0.0633 - val_accuracy: 0.0874
Epoch 51/750
29/29 [==============================] - 7s 227ms/step - loss: 6.7869e-04 - accuracy: 0.0409 - val_loss: 0.0479 - val_accuracy: 0.0874
Epoch 52/750
29/29 [==============================] - 7s 238ms/step - loss: 5.3305e-04 - accuracy: 0.0407 - val_loss: 0.0742 - val_accuracy: 0.0874
Epoch 53/750
29/29 [==============================] - 7s 227ms/step - loss: 5.2520e-04 - accuracy: 0.0233 - val_loss: 0.0530 - val_accuracy: 0.0874
Epoch 54/750
29/29 [==============================] - 7s 226ms/step - loss: 5.3272e-04 - accuracy: 0.0616 - val_loss: 0.0723 - val_accuracy: 0.1165
Epoch 55/750
29/29 [==============================] - 7s 231ms/step - loss: 6.7017e-04 - accuracy: 0.0443 - val_loss: 0.0581 - val_accuracy: 0.0874
Epoch 56/750
29/29 [==============================] - 7s 228ms/step - loss: 5.1613e-04 - accuracy: 0.0556 - val_loss: 0.0553 - val_accuracy: 0.0874
Epoch 57/750
29/29 [==============================] - 7s 228ms/step - loss: 4.8905e-04 - accuracy: 0.0410 - val_loss: 0.0544 - val_accuracy: 0.0874
Epoch 58/750
29/29 [==============================] - 7s 227ms/step - loss: 4.5829e-04 - accuracy: 0.0589 - val_loss: 0.0631 - val_accuracy: 0.0874
Epoch 59/750
29/29 [==============================] - 7s 227ms/step - loss: 5.8111e-04 - accuracy: 0.0770 - val_loss: 0.0622 - val_accuracy: 0.0874
Epoch 60/750
29/29 [==============================] - 7s 226ms/step - loss: 5.3620e-04 - accuracy: 0.0437 - val_loss: 0.0563 - val_accuracy: 0.0874
Epoch 61/750
29/29 [==============================] - 7s 227ms/step - loss: 6.3099e-04 - accuracy: 0.0548 - val_loss: 0.0564 - val_accuracy: 0.0874
Epoch 62/750
29/29 [==============================] - 7s 227ms/step - loss: 4.5764e-04 - accuracy: 0.0424 - val_loss: 0.0456 - val_accuracy: 0.1165
Epoch 63/750
29/29 [==============================] - 7s 227ms/step - loss: 5.3795e-04 - accuracy: 0.0627 - val_loss: 0.0570 - val_accuracy: 0.0874
Epoch 64/750
29/29 [==============================] - 7s 226ms/step - loss: 6.2734e-04 - accuracy: 0.0525 - val_loss: 0.0559 - val_accuracy: 0.0874
Epoch 65/750
29/29 [==============================] - 7s 227ms/step - loss: 4.9405e-04 - accuracy: 0.0544 - val_loss: 0.0530 - val_accuracy: 0.1165
Epoch 66/750
29/29 [==============================] - 7s 227ms/step - loss: 5.3328e-04 - accuracy: 0.0565 - val_loss: 0.0512 - val_accuracy: 0.0874
Epoch 67/750
29/29 [==============================] - 7s 226ms/step - loss: 4.6163e-04 - accuracy: 0.0577 - val_loss: 0.0697 - val_accuracy: 0.1165
Epoch 00067: early stopping

# Exporting the regressor
today = str(datetime.datetime.today().date())
params = ['reg', today, n_past_total, n_past, n_future, activation, n_layers, n_neurons, n_features, patience, optimizer]
filename = ''
for i in params:
    print(i)
    filename += str(i)
    if i!= params[-1]:
        filename += '_'
filename+='.h5'
regressor.save('model/{}'.format(filename))

reg
2021-06-09
1200
120
30
softsign
6
15
2
25
adam
INFO:tensorflow:Assets written to: model/filename\assets

list(res.history)

['loss', 'accuracy', 'val_loss', 'val_accuracy']

Validation

# Plotting Accuracy and Loss

results = res

history = results.history
plt.figure(figsize=(12,4))
plt.plot(history['val_loss'])
plt.plot(history['loss'])
plt.legend(['val_loss', 'loss'])
plt.title('Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show()

plt.figure(figsize=(12,4))
plt.plot(history['val_accuracy'])
plt.plot(history['accuracy'])
plt.legend(['val_accuracy', 'accuracy'])
plt.title('Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show()

def dummy_invscaler(y):
    '''
    Since the scaler was trained into 2 features, it needs two features to perform the inverse scaleer.
    For that purpose, this function will create a dummy array and concatenate it to the y_pred/y_true.
    That dummy of ones will be drop after performing the inverse_transform.
    INPUTS: array 'y', shape (X,)
    '''
    dummy = np.ones((len(y),1))
    y = np.array(y).reshape(-1,1)
    y = np.concatenate((y, dummy), axis=1)
    y = sc.inverse_transform(y)
    y = y[:,0]
    return y

# Validation (validation with the last training days -> -15-15 DAYS)

# getting the predictions
y_pred = regressor.predict(X_train[-1].reshape(1, n_past, n_features)).tolist()[0]
#dummy = np.ones((len(y_pred),1))
#y_pred = np.array(y_pred).reshape(-1,1)
#y_pred = np.concatenate((y_pred, dummy), axis=1)
#y_pred = sc.inverse_transform(y_pred)#.tolist()
#y_pred = y_pred[:,0]
y_pred = dummy_invscaler(y_pred)
#y_pred = sc.inverse_transform(np.array(y_pred).reshape(-1,1))#.tolist()

# getting the true values
#y_true = y_train[-1].reshape(-1,1)
#y_true = np.concatenate((y_true, dummy), axis=1)
#y_true = sc.inverse_transform(y_true)
#y_true = y_true[:,0]
y_true = dummy_invscaler(y_train[-1])


print('y_pred:\n', y_pred.tolist())
print('y_true:\n', y_true.tolist())

# plotting the results
plt.figure(figsize=(16,5))
plt.plot(y_pred, label='Predicted')
plt.plot(y_true, label='True')

dates = df_train.index[-n_future:]
dates = [str(dates.date[i]) for i in range(len(dates))]
plt.xticks(range(n_future),dates)

plt.title('BTC price Predicted vs True')
plt.legend()
plt.show()

y_pred:
 [26704.465916178073, 27517.522390125443, 27271.691896336917, 28508.68967540497, 29189.196689199645, 29472.05711862372, 30384.713827849453, 31163.14353121343, 31527.217648353115, 32469.32121186147, 33067.59974851268, 34330.41269297287, 35542.76815177513, 35962.06836683993, 38595.208052488466, 38799.0352444012, 39105.31845069016, 41550.42573775083, 41363.664969100886, 42657.42270480447, 42913.07420494965, 42985.521189661085, 44873.144475070316, 45797.00517801456, 45250.480902183954, 45792.3712723086, 46289.636052832386, 46354.23054306845, 46774.45753927444, 46921.09730573071]
y_true:
 [56048.9375, 58323.953125, 58245.00390599999, 59793.234375, 60204.96484400001, 59893.453125, 63503.45703099999, 63109.695313, 63314.01171900001, 61572.789063, 60683.82031299999, 56216.18359400001, 55724.265625, 56473.03125, 53906.08984400001, 51762.273438, 51093.652344, 50050.867188, 49004.253906, 54021.75390599999, 55033.117188, 54824.703125, 53555.109375, 57750.17578099999, 57828.05078099999, 56631.078125, 57200.29296900001, 53333.539063, 57424.007813, 56396.515625]

# Validation 2 (on the TEST SET -15 days)

# getting the predictions
x = df[['Close','Volume']][-n_past-n_future:-n_future].values
x = sc.transform(x)
y_pred = regressor.predict(x.reshape(1, n_past, n_features)).tolist()[0]
#y_pred = sc.inverse_transform(np.array(y_pred).reshape(-1,1))#.tolist()
y_pred = dummy_invscaler(y_pred)

# getting the true values
y_true = df['Close'][-n_future:].values

print('y_pred:\n', y_pred.tolist())
print('y_true:\n', y_true.tolist())

# plotting the results
plt.figure(figsize=(16,5))
plt.plot(y_pred, label='Predicted')
plt.plot(y_true, label='True')

dates = df.index[-n_future:]
dates = [str(dates.date[i]) for i in range(len(dates))]
plt.xticks(range(n_future),dates)

plt.title('BTC price Predicted vs True')
plt.legend()
plt.show()

y_pred:
 [26681.585557733037, 27494.210970684504, 27245.4007677618, 28477.462898426766, 29162.350569592305, 29444.927217194076, 30353.151182511938, 31131.438994964763, 31496.306982771905, 32438.604523728412, 33035.805407889864, 34290.834109199044, 35504.80245456098, 35922.6478387646, 38552.46835474468, 38753.11287963767, 39059.89899042185, 41500.02393080487, 41321.13361750512, 42608.65892964294, 42866.54117044192, 42936.297616103926, 44816.578495882946, 45737.96059810074, 45189.27024076573, 45740.22007615426, 46230.67768511775, 46298.33270842473, 46721.232282805315, 46867.17157514324]
y_true:
 [57356.40234400001, 58803.77734400001, 58232.31640599999, 55859.796875, 56704.57421900001, 49150.535156, 49716.191406, 49880.535156, 46760.1875, 46456.058594, 43537.511719, 42909.402344, 37002.441406, 40782.738281, 37304.691406, 37536.632813, 34770.582031, 38705.980469, 38402.222656, 39294.199219, 38436.96875, 35697.605469, 34616.066406, 35678.128906, 37332.855469, 36684.925781, 37575.179688, 39208.765625, 36894.40625, 35746.101563]

# evaluation
rmse = math.sqrt(mean_squared_error(y_true, y_pred))

mean_absolute_error(y_true, y_pred)

12059.573530830456

explained_variance_score(y_true, y_pred)
# Best possible score is 1.0, lower values are worse.

-2.350093924286009

Predictions

# Forecasting/Predicting

# getting the predictions
x = df[['Close','Volume']][-n_past:].values
x = sc.transform(x)
y_pred = regressor.predict(x.reshape(1, n_past, n_features)).tolist()[0]
#y_pred = sc.inverse_transform(np.array(y_pred).reshape(-1,1))#.tolist()
y_pred = dummy_invscaler(y_pred)

# creating a DF of the predicted prices
y_pred_df = pd.DataFrame(y_pred, 
                         index=pd.date_range(start=df[['Close']].index[-1]+datetime.timedelta(days=1),
                                             periods=len(y_pred), 
                                             freq="D"), 
                         columns=df[['Close']].columns)

# getting the true values
y_true_df = df[['Close']].tail(n_future)

# linking them
y_true_df = y_true_df.append(y_pred_df.head(1))

print('y_pred:\n', y_pred.tolist())
print('y_true:\n', y_true.tolist())

# plotting the results
plt.figure(figsize=(12,5))
plt.plot(y_pred_df, label='Predicted')
plt.plot(y_true_df, label='True')

#dates = df.index[-n_future:]
#dates = [str(dates.date[i]) for i in range(len(dates))]
#plt.xticks(range(n_future),dates)

plt.title('BTC price Predicted vs True')
plt.legend()
plt.show()

y_pred:
 [26655.66981144185, 27467.64683681198, 27219.366479696742, 28445.894864826798, 29131.46864141082, 29414.95051710399, 30320.0439019391, 31098.067689531938, 31462.750680328336, 32403.598778686122, 33000.63801497411, 34250.2676773096, 35462.953616208724, 35881.23006140825, 38502.99692111488, 38704.60774107368, 39012.27752689946, 41443.78124736443, 41270.124732989934, 42553.65913874068, 42814.181628139566, 42882.89993523644, 44755.601325837495, 45673.24038174087, 45128.066763697425, 45683.320024695066, 46171.10864765892, 46240.75014372202, 46663.27972408112, 46808.38563182697]
y_true:
 [57356.40234400001, 58803.77734400001, 58232.31640599999, 55859.796875, 56704.57421900001, 49150.535156, 49716.191406, 49880.535156, 46760.1875, 46456.058594, 43537.511719, 42909.402344, 37002.441406, 40782.738281, 37304.691406, 37536.632813, 34770.582031, 38705.980469, 38402.222656, 39294.199219, 38436.96875, 35697.605469, 34616.066406, 35678.128906, 37332.855469, 36684.925781, 37575.179688, 39208.765625, 36894.40625, 35746.101563]